English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Due to the limited availability of anomalous samples for training, videoanomaly detection is commonly viewed as a one-class classification problem.Many prevalent methods investigate the reconstruction difference produced byAutoEncoders (AEs) under the assumption that the AEs would reconstruct thenormal data well while reconstructing anomalies poorly. However, even with onlynormal data training, the AEs often reconstruct anomalies well, which depletestheir anomaly detection performance. To alleviate this issue, we propose asimple yet efficient framework for video anomaly detection. The pseudo anomalysamples are introduced, which are synthesized from only normal data byembedding random mask tokens without extra data processing. We also propose anormalcy consistency training strategy that encourages the AEs to better learnthe regular knowledge from normal and corresponding pseudo anomaly data. Thisway, the AEs learn more distinct reconstruction boundaries between normal andabnormal data, resulting in superior anomaly discrimination capability.Experimental results demonstrate the effectiveness of the proposed method.

Synthetic Pseudo Anomalies for Unsupervised Video Anomaly Detection: A Simple yet Efficient Framework based on Masked Autoencoder